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Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose-volume outcome
I El Naqa1, G Suneja, P E Lindsay
1Washington University, Saint Louis, MO, USA. ielnaqa@radonc.wustl.edu
Physics in Medicine and Biology
|October 28, 2006
Summary
This study introduces the Dose Response Explorer System (DREES), a free software tool for building radiotherapy outcome models. DREES helps analyze treatment, clinical, and biological factors to predict tumor control and normal tissue complications.
Area of Science:
- Radiation Oncology
- Biostatistics
- Medical Informatics
Background:
- Radiotherapy treatment outcomes depend on complex interactions between treatment parameters, clinical data, and biological factors.
- Accurate modeling of tumor control and normal tissue complications is crucial for optimizing radiotherapy.
- Existing tools may lack the flexibility and user-friendliness required for comprehensive outcome analysis.
Purpose of the Study:
- To develop an accurate, flexible, and user-friendly software tool for exploring radiotherapy outcomes data.
- To enable clinicians and scientists to build statistical models for tumor control probability (TCP) and normal tissue complication probability (NTCP).
- To provide an integrated environment for radiotherapy outcome analysis by combining DREES with other open-source tools.
Main Methods:
- The Dose Response Explorer System (DREES) was developed using Matlab, incorporating a named-field structure array data type.
- DREES integrates with the CERR ( aRadiotherapy Research) open-source tool for a comprehensive analysis environment.
- Key features include fitting analytical NTCP/TCP models, multi-term regression for dose-volume and clinical factors, automated variable selection using bootstrap resampling, parameter uncertainty estimation, and performance assessment using various statistical methods.
Main Results:
- DREES facilitates the fitting of analytical normal tissue complication probability (NTCP) and tumor control probability (TCP) models.
- The system supports combined modeling of multiple dose-volume variables and clinical factors through multi-term regression.
- It offers automated variable selection, uncertainty estimation, performance assessment (e.g., ROC curves, Kaplan-Meier plots), and visualization of prediction models.
Conclusions:
- DREES provides a customized and freely distributed software solution for radiotherapy outcome modeling.
- The tool empowers clinical researchers to analyze complex radiotherapy data and build predictive models.
- Future development of DREES will be guided by user feedback to enhance its capabilities.
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